Landslide susceptibility mapping using MT-InSAR and AHP enabled GIS-based multi-criteria decision analysis

Meghanadh, Devara; Tiwari, Ashutosh; Dwivedi, Ramji

doi:10.1080/19475705.2021.1887939

Cited by 43 publications

(18 citation statements)

References 34 publications

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“…The MT-InSAR analysis is a type of advanced InSAR data processing technique developed to overcome the limitations of the classical differential InSAR method, such as temporal and geometrical decorrelation, as well as to compensate for errors caused by atmospheric distortions, inaccurate terrain models, and uncertain satellite orbits [89][90][91]. This method's ability to detect and investigate deformation phenomena has already been demonstrated in various applications [92][93][94][95][96][97][98][99][100]. Furthermore, since our study area is mostly covered by arable fields and forests (Figure 1C), we could retrieve reliable results of ground movements by using the MT-InSAR approach, which can be successfully applied for agricultural and non-urban areas [91].…”

Section: Study Area and Input Datamentioning

confidence: 99%

Estimation of Mining-Induced Horizontal Strain Tensor of Land Surface Applying InSAR

et al. 2021

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Horizontal strains related to mining-induced subsidence may endanger infrastructure and surface users’ safety. While directional horizontal strains should be well determined, appropriate solutions for a complete assessment of the terrain surface deformation field are still required. As a result, the presented study examined a new method for calculating horizontal strain tensor based on the decomposition of satellite radar interferometry (InSAR) observations into vertical and azimuth look direction (ALD) displacements. Based on a geometric integral model, we tested our method on experimental data before applying it to an underground copper ore mine in Poland. In the case study, the displacement field was determined using the Multi-Temporal InSAR method on Sentinel-1 data. The model data relative error did not exceed 0.02 at σ = ±0.003. For the case study, land subsidence of up to −167 mm and ALD displacements ranging from −110 mm to +62 mm was obtained, whereas the extreme values of horizontal strains ranged from −0.52 mm/m to +0.36 mm/m at σ = ±0.050 mm/m. Our results demonstrate the high accuracy of the method in determining the horizontal strain tensor. As a result, the approach can broaden the assessment of the environmental impact of land subsidence worldwide.

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Section: Study Area and Input Datamentioning

confidence: 99%

Estimation of Mining-Induced Horizontal Strain Tensor of Land Surface Applying InSAR

et al. 2021

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“…Identifying and mapping an acceptable collection of conditioning factors connected to landslide episodes requires prior knowledge of the principal landslide contributors [40]. In general, the number of landslide conditioning factors considered varies from several to dozens [41]. Based on prior studies and the features of landslide development in the study area, eleven elements were selected under five categories in order to identify the important conditioning factors of landslide susceptibility.…”

Section: Landslide Conditioning Factors (Lcfs)mentioning

confidence: 99%

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Shahabi¹,

Tavakolifar²,

Alizadeh³

et al. 2023

Preprint

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Landslides around the main roads in the mountains not only cause fatal events but also cause ecosystem damage, including land degradation. This study aims to map the susceptibility of the landslides around the Saqqez-Marivan main rod of Kurdistan province, Iran, using ensemble Fuzzy logic with Analytic Network Process (Fuzzy Logic-ANP; FLANP), and with TOPSIS (Fuzzy Logic-TOPSIS; FLTOPSIS). A total of 100 landslides were first recognized by field surveys and then they were randomly divided into a 70% dataset (70 locations) and a 30% dataset (30 locations), respectively, for training and validating the methods. Eleven landslide conditioning factors, including slope, aspect, elevation, lithology, land use, distance to fault, distance to a river, distance to road, soil type, curvature, and precipitation were used. The performance of the methods was checked by the areas under the receiver operating curve (AUCROC). Results concluded that the prediction accuracy based on validating datasets were, respectively, 0.882 and 0.918 for FLANP and FLTOPSIS methods. Our findings demonstrated that although both models were known as promising techniques, the FLTOPSIS method had a better capacity for predicting the susceptibility of landslides in the studied area. Therefore, the susceptibility map developed by the FLTOPSIS method can be used for the proper management of areas with high landslide potential and also for managers and planners during the implementation of land allocation and development projects, especially in mountainous areas.

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“…The AHP method was developed by Thomas L. Saaty (Sur et al, 2020) Many researchers have implemented weighted overlay analysis to produce a landslide susceptibility map (Devara et al, 2021). It is a method of creating an integrated analysis by applying a single scale of values to varied and distinct sources using visual or analytical operations (Tiwari & Ajmera, 2021).…”

Section: Analytical Hierarchy Process (Ahp) Modelmentioning

confidence: 99%

Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan

Ahmad

Shao

Aslam

et al. 2022

Stoch Environ Res Risk Assess

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The notion of this research is based on the two devastating earthquake events that happened on October 8, 2005, and September 24, 2019, in the regions of Azad Kashmir. This study aims at (i) identi cation of the susceptible zones where landslides will occur in the future; (ii) preparation of landslide inventory maps using vector data, satellite imagery, Shuttle Radar Topographic Mission (STRM) and Advanced Space-borne Thermal Emission and Re ection Radiometer (ASTER) DEM; (iii) implementation of Analytical Hierarchy Process (AHP) model using weighted overlay analysis (WOA). For this purpose, key factors such as land-use, faults, slope, contours, soil and seismology are used to develop a landslide hazard zonation map. The output landslide susceptibility map has four susceptibility levels such as low, medium, high, and very high vulnerable zones. The results indicated that a highly susceptible landslide zone is found in the northwestern part of Muzaffarabad, which is a metropolitan region. Moreover, there are 127 active landslides are identi ed and collectively about 9% of the study area is very highly susceptible to future landslides. Moreover, research ndings are helpful in tactful thinking for future infrastructure development, ecological protection in high-susceptible landslide regions in Muzaffarabad.It also allows the Government to make strategies for that speci c zones on a priority basis to reduce the casualties and destruction in future landslide events.

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Landslide susceptibility mapping using MT-InSAR and AHP enabled GIS-based multi-criteria decision analysis

Cited by 43 publications

References 34 publications

Estimation of Mining-Induced Horizontal Strain Tensor of Land Surface Applying InSAR

Estimation of Mining-Induced Horizontal Strain Tensor of Land Surface Applying InSAR

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan

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